Relevance of learning analytics to measure and support students’ learning in adaptive educational technologies

TitleRelevance of learning analytics to measure and support students’ learning in adaptive educational technologies
Publication TypeConference Paper
AuthorsBannert M, Molenaar I, Azevedo R, Järvelä S, Gasevic D
Abstract

In this poster, we describe the aim and current activities of the EARLI-Centre for Innovative Research (E-CIR) “Measuring and Supporting Student’s Self-Regulated Learning in Adaptive Educational Technologies” which is funded by the European Association for Research on Learning and Instruction (EARLI) from 2015 to 2019. The aim is to develop our understanding of multimodal data that unobtrusively capture cognitive, meta-cognitive, affective and motivational states of learners over time. This demands for a concerted interdisciplinary dialogue combining findings from psychology and educational sciences with advances in computer sciences and artificial intelligence. The participants in this E-CIR are leading international researchers who have articulated different emerging perspectives and methodologies to measure cognition, metacognition, motivation, and emotions during learning. The participants recognize the need for intensive collaboration to accelerate progress with new interdisciplinary methods including learning analytics to develop more powerful adaptive educational technologies.In this poster, we describe the aim and current activities of the EARLI-Centre for Innovative Research (E-CIR) “Measuring and Supporting Student’s Self-Regulated Learning in Adaptive Educational Technologies” which is funded by the European Association for Research on Learning and Instruction (EARLI) from 2015 to 2019. The aim is to develop our understanding of multimodal data that unobtrusively capture cognitive, meta-cognitive, affective and motivational states of learners over time. This demands for a concerted interdisciplinary dialogue combining findings from psychology and educational sciences with advances in computer sciences and artificial intelligence. The participants in this E-CIR are leading international researchers who have articulated different emerging perspectives and methodologies to measure cognition, metacognition, motivation, and emotions during learning. The participants recognize the need for intensive collaboration to accelerate progress with new interdisciplinary methods including learning analytics to develop more powerful adaptive educational technologies.